Procedure for predicting a dichotomous outcome and output predicted values

[seisel]

I would like to get a model to predict the dichotomous yes/no outcome (yes sleep disturbance/ no sleep disturbance) and then output that to a new dataset for each individual at each time point (is this possible?). There are many individuals that do not have all 4 time points and we would like to be able to include everyone. So far I have only been able to get the model to output means/probabilities and they are the same for each participant and each time point (or just for individuals with data at that time point). Any suggestions on what model/code I should be using? For example, if my data and model look like this:

data sleep;

input ID Months SleepDist;

cards;

1         0         1

1         10        1

1         25        0

1         39        1

2         0         1

2         10        .

2         25        1

2         39        0

3         0         1

3         10        .

3         25        0

3         39        .

4         0         1

4         10        .

4         25        0

4         39        0

5         0         1

5         10        1

5         25        1

5         39        0

6         0         0

6         10        .

6         25        0

6         39        1

;

proc genmod data= sleepdata;

   class ID /missing;

   model SleepDist = Months / dist=bin expected;

   repeated subject=ID / type=un covb corrw;

          output out = try

                             pred= pred;

run;

What I get:

ID	Months	SleepDist	Predicted Value
1	0	1	0.164
1	10	1	0.269
1	25	0	0.485
1	39	1	0.694
2	0	1	0.164
2	10	.	0.269
2	25	1	0.485
2	39	0	0.694
3	0	1	0.164
3	10	.	0.269
3	25	0	0.485
3	39	.	0.694

...

I would like:

ID	Months	SleepDist	Predicted Value
1	0	1	1
1	10	1	1
1	25	0	0
1	39	1	1
2	0	1	1
2	10	.	1
2	25	1	1
2	39	0	0
3	0	1	1
3	10	.	0
3	25	0	0
3	39	.	1

…

Thanks!

[Reeza]

You need to decide what your cut off is. Most classification algorithms give you a probability and then you use a cutoff to convert that to a 0/1. Ie if predicted value > 0.7 then 1, else 0. Or 0.5, or 0.3. You usually pick the cutoff by looking at the classification tables or the ROC graphs.

https://www.researchgate.net/post/How_do_I_calculate_the_best_cutoff_for_ROC_curves

[PGStats]

Also, be careful what probability you are modelling. For a neutral cutoff (0.5) you would be doing:

proc genmod data=sleep;
   class ID;
   model SleepDist(event='1') = Months / dist=bin expected;
   repeated subject=ID / type=un covb corrw;
   output out = sleepPred pred=pred;
run;

data sleepProb;
set sleepPred;
SleepDistPred = pred > 0.5;
run;

(event='1') tells the procedure which probability you are modelling.